Digital medical images are reconstructed using raw image data obtained from a scanner. One type of scanner is a flat panel detector, which is a solid-state X-ray digital radiography device. To obtain the image data, X-rays are passed through the subject being imaged and sensed by the flat panel detector. Flat-panel detectors are more sensitive and faster than other types of imaging, such as film. However, bad, failing or broken (i.e., “defective”) pixels in the flat panel detector may result in ring artifacts in reconstructed volumetric images, thereby compromising image quality.
Such “bad pixel” problem may be solved by two different approaches. In a first approach, when a detector is delivered or during required maintenance, a service engineer performs a number of test image acquisitions with varying detector exposures to determine and calibrate the detector for offset, gain and defective (or bad) pixels. While detector offset and gain vary only minimally with time, the failure of detector elements resulting in defective image pixels is a frequently occurring event. A defective pixel map may be created for each detector to identify defective pixels. However, it is too time-consuming to calibrate each detector for every failing element.
Another approach is to correct pixels (or voxels) in the volumetric image reconstructed from the raw image data. The bad pixels are represented as dark or bright circles or artifacts in the reconstructed image. Different methods may be used to identify these ring-like structures and interpolate the corrupt image data. However, this ‘after the fact’ processing approach presents the challenges of misidentifying these artifacts and interpolating larger areas of the reconstructed image.